Data Characteristics in Pharmacovigilance
Pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE), individual case safety reports (ICSRs), medical literature, and safety updates from regulatory bodies. This data exists as a mix of structured formats (e.g., CIOMS I forms, MedDRA codes) and unstructured formats (e.g., free-text descriptions, medical imaging reports). Update frequencies vary; ICSR reports may update daily, while regulatory safety signal analysis reports might be quarterly or annual. Document lengths differ significantly, ranging from hundreds of words for case reports to tens of thousands for clinical study summaries. Fields include patient demographics, drug information, adverse event descriptions, diagnoses, treatments, and outcomes. Adverse event descriptions often involve medical terminology and professional abbreviations.
Constraints on Citation and Traceability from Data Characteristics
The diversity of pharmacovigilance data sources requires flexible citation capabilities to accommodate various document formats. High-frequency ICSR data demands real-time or near real-time data synchronization to ensure the timeliness of traceability information. The mix of structured and unstructured data necessitates both field-level precise matching and semantic-level contextual understanding for accurate identification of adverse event-drug associations. The prevalence of medical terminology and professional abbreviations in adverse event descriptions challenges text processing accuracy, requiring models with specialized medical knowledge to prevent traceability errors due to term misinterpretation. Varying document lengths require segmentation strategies that adapt to different granularities, from short texts to lengthy reports.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Balances contextual coherence for long medical reports with model processing efficiency. |
Recall Count | Top 5 | Balances coverage of multiple information sources with reduction of irrelevant noise, focusing on core adverse event information. |
Similarity Threshold | 0.75 | Improves accuracy in matching medical terminology, preventing incorrect citation of irrelevant clinical symptoms. |
Rerank Return Count | 3 | Highlights the most relevant adverse event reports or safety signals, optimizing user experience. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large clinical trial reports or regulatory documents, preventing timeouts. |
Segment Length | 300 characters | Ensures completeness of adverse event descriptions while preventing individual segments from becoming too long. |
Common Misconfigurations
- Citation results include a large amount of irrelevant patient history information. This occurs when
maxContextis set too high, causing the model to include non-core content in the context. - When processing medical literature, the system reports an "input error" or returns empty results. This happens when the plugin fails to correctly parse PDFs or specific formats of medical image reports. This may be due to a
PARSE_FILE_TIMEOUT_SECONDSvalue that is too low or a lack of support for specific file types. - Traced adverse event reports show weak relevance to the actual queried drug, with too many returned results but little effective information. This occurs when the
Similarity Thresholdis set too low, leading to the recall of many generic medical texts.
How to Verify Configuration
- Select a pharmacovigilance report containing typical adverse event descriptions. Query for a specific drug or symptom and verify that the citations accurately point to key paragraphs in the report.
- Upload a clinical trial summary containing complex medical terms and abbreviations. Observe if the parsing process is smooth and check if the citation results correctly interpret these terms.
- Select multiple reports with similar adverse events but involving different drugs. Perform cross-queries to assess the system's ability to distinguish subtle differences and trace back to the correct drug.
The values provided are common starting points. Measure them against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.